Structured query language (SQL) is a kind of database query and programming language which is used to store, retrieve and query the data held in the relational database system.
The sequential query language's (SQL) development began in 1974, proposed by Boyce and Chamberlin, at that time called SEQUEL; In 1976, IBM Corporation's Sanjase research institute when developed RDBMS SYSTEM R changes SQL; ORACLE Corporation published first in 1979 based on the SQL commercialization RDBMS product; American country Standardization organization ANSI in 1986 announced that SQL takes the database industrial standard. It is well known that the SQL language contains four parts:                Data definition language (DDL), contains CREATE, DROP, ALTER.        Data manipulation language (DML) contains INSERT, UPDATE and DELETE.        Data Query language (DQL), contains SELECT.        Data management language (DCL) contains SQL statements such as GRANT, REVOKE, COMMIT, and ROLLBACK.        
Any SQL statement is processed by the RDBMS in the following steps:                1. Parse (Syntax Check): It parses the SQL statement syntax and decides whether it conform to the standard.        2. Analyze: It checks whether the objects (tables, columns, etc.) used in SQL statement exist in Database. This phase will extract any bind variable if there are any.        3. Optimize: It chooses the best plan for query execution based on cost.        4. Execute: It executes the best plan generates in previous step and returns the result.        
During query execution, Indexing is one of the important data structures that improve the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index data structure. INDEX is created on particular column of a table to keep the tuples organized and hence access of those tuple becomes faster. Index data segment stores only the value of column being part of index, and all other fields of table are stored in heap data segment. During creation of index only, it becomes clear that what data-type value is going to be stored in index data segment. Since the data-type of indexed column is known during its creation, so all of the access method to various operation on index also remains constant. Some of the top operation done on indexes may include but not limited to access of index tuple, finding slot to insert a particular index tuple, search of any index tuple, and the like.
In the recent past to improve the performance of databases, the approach identified is to reduce the CPU instructions for a given operation. In this context there are works available in the prior-art where a query is compiled into native format and then executed. A further improvement of this technology is to optimize the query execution by compiling access method to a table. In this case the performance of access is improved. MemSQL, Micro-Specialization in DBMSes, SQL Server 2014 are few examples of such specialization. These solutions offer mechanism to compile table schema to native code during “TABLE CREATION” time.
In order to do operation like INSERT on INDEX column, according to prior-art techniques available, first it finds the appropriate position for example in B-Tree, where it can store the new data and still maintaining the sorted order of data. Similarly to do SEARCH operation on any index, it does the comparison of the node (e.g. binary search for BTree index). For index the comparison and infrastructure function constitute most of index cost. All these comparison and infrastructure functions are implemented in a very generic way to handle all data-types of columns and all kinds of INDEX.